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---
library_name: transformers
license: mit
base_model: DeepMount00/Italian-ModernBERT-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: modernbert-italian-finetuned-ner
results: []
datasets:
- tner/wikiann
language:
- it
pipeline_tag: token-classification
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# modernbert-italian-finetuned-ner
This model is a fine-tuned version of [DeepMount00/Italian-ModernBERT-base](https://huggingface.co/DeepMount00/Italian-ModernBERT-base) on [tner/wikiann](https://huggingface.co/datasets/tner/wikiann) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0422
- Precision: 0.9339
- Recall: 0.9452
- F1: 0.9395
- Accuracy: 0.9909
## Model description
Token classification for italian language experiment, NER.
### Example
```python
from transformers import pipeline
ner_pipeline = pipeline("ner", model="nickprock/modernbert-italian-finetuned-ner", aggregation_strategy="simple")
text = "La sede storica della Olivetti è ad Ivrea"
output = ner_pipeline(text)
```
## Intended uses & limitations
The model can be used on token classification, in particular NER. It is fine tuned on italian language.
## Training and evaluation data
The dataset used is [wikiann](https://huggingface.co/datasets/tner/wikiann)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0277 | 1.0 | 11050 | 0.0324 | 0.9233 | 0.9362 | 0.9297 | 0.9899 |
| 0.0139 | 2.0 | 22100 | 0.0341 | 0.9327 | 0.9428 | 0.9377 | 0.9907 |
| 0.0052 | 3.0 | 33150 | 0.0422 | 0.9339 | 0.9452 | 0.9395 | 0.9909 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0 |